This research investigates manganese (Mn) extraction from Electric Arc Furnace Steel Slag (EAFS) by using the Liquid-liquid extraction (LLE) method. The chemical analysis was done on the slag using X-ray fluorescence, X-ray diffraction, and atomic absorption spectroscopy. This work consisted of two parts: the first was an extensive study of the effect of variables that can affect the leaching process rate for Mn element from slag (reaction time, nitric acid concentration, solid to liquid ratio, and stirring speed), and the second part evaluates the extraction of Mn element from leached solution. The results showed the possibility of leaching 83.5 % of Mn element from the slag at a temperature of 25°C, nitric acid concentration 2 M, time 90 min, S / L ratio 1/100, and stirring speed 700 rpm. 94.7% extraction of Mn was accomplished from nitric acid solutions by using Octyl Pyro Phosphoric Acid (OPPA) in kerosene at contact time for 12 min, 50%OPPA -kerosene, stirring speed 900 rpm, and organic to the aqueous phase (O/A) of 4/1. Kerosene was the most important diluting agent in extracting Mn, compared to benzene and toluene.
This study exposed to use the liquid whey (which was produced from of soft cheese processed) partially or completely instead of milk in fatty cake, this whey residue is still not used, instead it is thrown in rivers which effect different environment and economic problems. Different concentrations was used (25% , 50% , 75% , and 100%) of whey in baked cake , Volume , height and other different properties ( panel taste ) was studied too . Sensory evaluation results showed that an improved in all the character of the baked cake was happen by the used of 25% and 50% of the whey in comparison with the control treatment, the 75% replacement showed a decrease in appearance , texture and tenderness , while the degrees of color and fla
... Show MoreThe aim of present study was to develop solid and liquid self-microemulsifying drug delivery system of poorly water soluble drug mebendazole using Aerosil 200 as solid carrier. Microemulsions are clear, stable, isotropic liquid mixtures of oil, water and surfactant, frequently in combination with a co-surfactant having droplet size range usually in the range of 20-250 nm. Oleic acid, tween 80 and polypropylene glycol were selected as oil, surfactant and co-surfactant respectively and for preparation of stable SMEDDS, micro emulsion region was identified by constructing pseudo ternary phase diagram containing different proportion of surfactant: co-surfactant (1:1, 2:1 and 3:1), oil and water. In brief S/ CoS mix means su
... Show MoreAn investigation was conducted for the improvement of viscosity index of light lubricating oil fraction (40 stock)
obtained from vacuum distillation unit of lube oil plant of Daura Refinery, using solvent extraction process.
In this study furfural solvent was used to extract the undesirable materials which reduce the viscosity index of raw
lubricating oil fraction.
The studied effecting variables of extraction were extraction temperature range from 70 to 110°C, and solvent to oil
ratio range from 1:1 to 4:1 (wt/wt).
The n-d-M method was used for calculation of carbon distribution and structural group analysis of the raffinate
produced from furfural extraction.
Also the three component phase diagram for a mixed-ba
This study aims to show, the strength of steel beam-concrete slab system without using shear connectors (known as a non-composite action), where the effect of the friction force between the concrete slab and the steel beam has been investigated, by using finite element simulation.
The proposed finite element model has been verified based on comparison with an experimental work. Then, the model was adopted to study the system strength with a different steel beam and concrete slab profile. ABAQUS has been adopted in the preparation of all numerical models for this study.
After validation of the numerical models, a parametric study was conducted, with linear and non-linear Regression analysis. An equation re
... Show MoreIn the image processing’s field and computer vision it’s important to represent the image by its information. Image information comes from the image’s features that extracted from it using feature detection/extraction techniques and features description. Features in computer vision define informative data. For human eye its perfect to extract information from raw image, but computer cannot recognize image information. This is why various feature extraction techniques have been presented and progressed rapidly. This paper presents a general overview of the feature extraction categories for image.
Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
... Show MoreOil from Brassca campestris (local variety) was extracted with hexane using Soxhlet. The extracted oil was characterized and its antimicrobial activity was determined as well. The content of extracted oil was 40% with 0.5% of volatile oil .Oil was immiscible with polar solvent such as ethanol, acetone and water, while it was easily miscible with chloroform due to its hydrophobicity. The result of organoleptic tests revealed that the oil is clear yellow in color and odorless with acceptable taste. The oil was stable at 4 -25 C? for a month. Refractive index (RI) of oil was 1.4723 with density of 0.914, [both at 4-25 C?]. Boiling point 386 C?. Infra red spectroscopy (IR) indicated the presence of different chemical groups (C=C
... Show MoreImage classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
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